Short-term space safety analysis of LEO constellations and clusters

Rossi, Alessandro; Petit, Alexis; McKnight, Darren

doi:10.1016/j.actaastro.2020.06.016

Cited by 22 publications

(6 citation statements)

References 8 publications

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“…The short-term collision probability orbit prediction model uses the SGP4 model to simulate the first phase of the Starlink constellation and to facilitate the prediction of the orbit of the space cataloging target [30]. The constellation parameters of the first phase of Starlink are 1584/72/33, the orbit inclination is 53°, and the orbit height is 550 km.…”

Section: Numerical Experimental Setupmentioning

confidence: 99%

Collision risk analysis of mega constellations in low Earth orbit

Ruan,

Hu,

Yun

et al. 2023

Preprint

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The LEO megaconstellations have thousands of satellites, which operate on similar orbital heights. Because of increasing space debris, the satellites accelerate the growth of the number of space objects, increase the threat to their own safety and sustainable utilization of the space environment. This paper focuses on short-term collision probability and long-term collision probability calculation model for collision risk of mega constellations. On the one hand, aiming at how to solve the problem quickly and accurately for rendezvous collisions, a short-term collision risk evolution calculation model of LEO mega-constellation is established. By investigating the space collision target screening methods of large-scale satellites against large-scale space debris, including apogee–perigee filtering, time window filtering, space discrete volume element filtering and relative position filtering, the filtered target trajectory is fitted by Lagrange interpolation method to obtain the motion state at the closest time. Then, the collision probability calculation method of Laplace transform is used to calculate the collision probability of the target at the closest time. On the other hand, the long-term collision risk evolution model of LEO Mega constellation is established. By simplifying the model of debris and collision, we explore the calculation method of space object density, space debris attenuation model, space target disintegration model and the distribution of disintegration targets are studied. According to the way of space debris’ generation and extinction to calculate the change of space density. The collision probability between the satellite and space debris and space debris are calculated by using the calculation method of collision probability of particle-in-a-box (PIB). Finally, through the simulation of Starlink phase I constellation, results show that after the deployment of the Starlink constellation, the probability of a short-term collision in the constellation shell increases by 30–40%, the probability of at least one collision in the constellation lifetime is 70.2%, and the probability of a secondary collision increases by 25.3% after a collision. This study provides a theoretical reference for analyzing the orbital safety of the LEO mega-constellation, and provides a reference for the safe operation of spacecraft on-orbit and the sustainable development and utilization of space resources.

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Section: Numerical Experimental Setupmentioning

confidence: 99%

Collision risk analysis of mega constellations in low Earth orbit

Ruan,

Hu,

Yun

et al. 2023

Preprint

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“…Large clusters of derelict LEO rocket bodies have been identified as high priority candidates for future ADR missions (Liou 2011;Rossi et al 2020;McKnight et al 2021;Maclay & Mcknight 2021). Another key target is ESA's defunct eight-tonne satellite, Envisat, which resides in a densely populated region and is known to be tumbling (Kucharski et al 2014;Pittet et al 2018).…”

Section: Fixes For the Futurementioning

confidence: 99%

Looking out for a sustainable space

Blake

2022

Preprint

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“…Simulations of the long-term evolution of debris suggest that LEO is already in the protracted initial stages of the Kessler Syndrome but that this could be managed through active debris removal [ 25 ]. The addition of satellite mega-constellations and the general proliferation of low-cost satellites in LEO stresses the environment further [ 26 , 27 , 28 ]. Hence, decades of the world’s space activities have left LEO cluttered with active satellites and littered with orbital debris.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Massimi

Ferrara²,

Benedetto

2022

Sensors

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Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices’ learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues.

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Short-term space safety analysis of LEO constellations and clusters

Cited by 22 publications

References 8 publications

Collision risk analysis of mega constellations in low Earth orbit

Collision risk analysis of mega constellations in low Earth orbit

Looking out for a sustainable space

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

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